def realTimeTwitterMap(interval):
"""
Interval in minutes!
"""
while True:
df = pd.read_csv('terrortracking.csv',encoding="Latin-1")
arr = df[['Lat', 'Long']]
arr=arr.dropna()
arr=(np.array(arr)).tolist()
maps = gmaps.heatmap(arr)
gmaps.display(maps)
print('done')
time.sleep(interval*60)
def plotRiskyLocations(name, country):
if name == None:
return
# Consider saving this to pickle file.
dic = {
"Business": ["Business", "Gas", "mall", "restaurant", "cafe", "hotel"],
"Government (General)": ["Government buildings", "Ministry"],
"Police": ["Police post", "prison", "Police"],
"Military": ["military base", "air base", "navy"],
"abortion related": "abortion clinic",
"airports & aircraft": "airport",
"Government (Diplomatic)": "embassy",
"Educational Institution": ["school", "university"],
"Food or Water Supply": ["water treatment plant", "farms"],
"NGO": ["NGO", "Non governmental organisations"],
"Maritime": ["port", "ferry"],
"Journalists & Media": "newspaper company",
"Other": ["fire station", "hospital"], # wtf do you code for this
"Private Citizens & Property": ["shopping malls", "markets"],
"Religious Figures/Institutions": ["temples", "churches", "mosques"],
"Terrorists/Non-State Militia": "militia",
"Transportation": ["Train station", "Bus stations"],
"Utilities": ["power plant", "water plant"],
"Tourists": ["tourist spots"],
"Telecommunications": ["Radio station", "TV station", "Internet provider"],
"Violent Political Party": "political party", # and this
}
name = dic[name]
geolocator = Nominatim()
location = []
if type(name) != list:
loc = geolocator.geocode(name + " " + country, exactly_one=False, timeout=10)
if loc is not None:
location.append(loc)
else:
for entry in name:
loc = geolocator.geocode(entry + " " + country, exactly_one=False, timeout=10)
if loc is not None:
location.append(loc)
location = list(itertools.chain(*location)) # flatten list
data = []
for entry in location:
data.append([entry.latitude, entry.longitude])
maps = gmaps.heatmap(data)
gmaps.display(maps)
# ## Creazione della mappa
#
# invece che uno scatterplot con dei raggi, la libreria ci consente solo di fare una heatmap (eventualmente pesata)
#
# In[2]:
roma = pandas.read_csv("../data/Roma_towers.csv")
coordinate = roma[['lat', 'lon']].values
# In[3]:
heatmap = gmaps.heatmap(coordinate)
gmaps.display(heatmap)
# TODO scrivere che dietro queste due semplici linee ci sta un pomeriggio intero di smadonnamenti
# In[4]:
colosseo = (41.890183, 12.492369)
# In[5]:
import gmplot
from gmplot import GoogleMapPlotter
# gmap = gmplot.from_geocode("San Francisco")
import gmaps
# load a Numpy array of (latitude, longitude) pairs
data = gmaps.datasets.load_dataset('taxi_rides')
map = gmaps.heatmap(data)
gmaps.display(map)